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Vasunilashorn SM, Lunardi N, Newman JC, Crosby G, Acker L, Abel T, Bhatnagar S, Cunningham C, de Cabo R, Dugan L, Hippensteel JA, Ishizawa Y, Lahiri S, Marcantonio ER, Xie Z, Inouye SK, Terrando N, Eckenhoff RG. Preclinical and translational models for delirium: Recommendations for future research from the NIDUS delirium network. Alzheimers Dement 2023; 19:2150-2174. [PMID: 36799408 PMCID: PMC10576242 DOI: 10.1002/alz.12941] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 12/19/2022] [Accepted: 12/20/2022] [Indexed: 02/18/2023]
Abstract
Delirium is a common, morbid, and costly syndrome that is closely linked to Alzheimer's disease (AD) and AD-related dementias (ADRD) as a risk factor and outcome. Human studies of delirium have advanced our knowledge of delirium incidence and prevalence, risk factors, biomarkers, outcomes, prevention, and management. However, understanding of delirium neurobiology remains limited. Preclinical and translational models for delirium, while challenging to develop, could advance our knowledge of delirium neurobiology and inform the development of new prevention and treatment approaches. We discuss the use of preclinical and translational animal models in delirium, focusing on (1) a review of current animal models, (2) challenges and strategies for replicating elements of human delirium in animals, and (3) the utility of biofluid, neurophysiology, and neuroimaging translational markers in animals. We conclude with recommendations for the development and validation of preclinical and translational models for delirium, with the goal of advancing awareness in this important field.
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Affiliation(s)
- Sarinnapha M. Vasunilashorn
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Nadia Lunardi
- Department of Anesthesiology, University of Virginia, Charlottesville, Virginia, USA
| | - John C. Newman
- Department of Medicine, University of California, San Francisco, California, USA
- Buck Institute for Research on Aging, Novato, California, USA
| | - Gregory Crosby
- Harvard Medical School, Boston, Massachusetts, USA
- Department of Anesthesiology, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Leah Acker
- Department of Anesthesiology, Duke University, Durham, Massachusetts, USA
| | - Ted Abel
- Department of Neuroscience and Pharmacology, Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA
| | - Seema Bhatnagar
- Department of Anesthesiology and Critical Care, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Department of Anesthesiology and Critical Care, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Colm Cunningham
- School of Biochemistry and Immunology, Trinity Biomedical Sciences Institute, Dublin, Ireland
- Trinity College Institute of Neuroscience, Trinity College, Dublin, Ireland
| | - Rafael de Cabo
- Translational Gerontology Branch, Intramural Research Program, National Institute on Aging, Baltimore, Maryland, USA
| | - Laura Dugan
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, Tennessee, USA
- Division of Geriatric Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- VA Tennessee Valley Geriatric Research, Education, and Clinical Center (GRECC), Nashville, Tennessee, USA
| | - Joseph A. Hippensteel
- Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Yumiko Ishizawa
- Harvard Medical School, Boston, Massachusetts, USA
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Shouri Lahiri
- Department of Neurology, Neurosurgery, and Biomedical Sciences, Cedar-Sinai Medical Center, Los Angeles, California, USA
| | - Edward R. Marcantonio
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
- Marcus Institute for Aging Research, Hebrew SeniorLife, Boston, Massachusetts, USA
| | - Zhongcong Xie
- Harvard Medical School, Boston, Massachusetts, USA
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Sharon K. Inouye
- Harvard Medical School, Boston, Massachusetts, USA
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
- Marcus Institute for Aging Research, Hebrew SeniorLife, Boston, Massachusetts, USA
| | - Niccolò Terrando
- Department of Anesthesiology, Duke University, Durham, North Carolina, USA
- Department of Cell Biology, Duke University, Durham, North Carolina, USA
- Department of Immunology, Duke University, Durham, North Carolina, USA
- Duke Center for the Study of Aging and Human Development, Duke University School of Medicine, Durham, USA
| | - Roderic G. Eckenhoff
- Department of Anesthesiology and Critical Care, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
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Kunkel D, Parker M, Pearce RA, Sanders RD, Lennertz RC. Plasma HMGB1 levels in postoperative delirium. Br J Anaesth 2023; 130:e449-e451. [PMID: 36858886 PMCID: PMC10273084 DOI: 10.1016/j.bja.2023.02.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 01/19/2023] [Accepted: 02/01/2023] [Indexed: 03/03/2023] Open
Affiliation(s)
- David Kunkel
- Department of Anesthesiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Margaret Parker
- Department of Anesthesiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Robert A Pearce
- Department of Anesthesiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Robert D Sanders
- Department of Anaesthetics, University of Sydney, Sydney, NSW, Australia; Department of Anaesthetics, Royal Prince Alfred Hospital, Camperdown, NSW, Australia
| | - Richard C Lennertz
- Department of Anesthesiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
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Chen C, Wen M, Jin Y. 1DE-MS Profiling for Proteoform-Correlated Proteomic Analysis, by Combining SDS-PAGE, Whole-Gel Slicing, Quantitative LC-MS/MS, and Reconstruction of Gel Distributions of Several Thousands of Proteins. J Proteome Res 2022; 21:2311-2330. [PMID: 36018058 DOI: 10.1021/acs.jproteome.2c00180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
SDS-PAGE has often been used in proteomic analysis, but generally for sample prefractionation although the technique separates proteins by molecular masses (Mws) and the information would contribute to proteoform-level analysis. Here, we report a method that combines SDS-PAGE, whole-gel slicing, and quantitative LC-MS/MS for establishing gel distributions of several thousand proteins in a proteome. A previously obtained data set on rat cerebral cortex with cerebral ischemia-reperfusion injury1 was analyzed, and the gel distributions of 5906 proteins were reconstructed. These distributions, referred to as 1DE-MS profiles, revealed that about 30% of the proteins had more than one proteoform detected in the gels. The profiles were categorized into six types by distribution (narrow, dispersed, or broad) and relative deviations between the abundance-peak apparent Mws and calculated Mws. Only 56% of the proteins showed narrow distributions and matched Mws, while the others had rather complex profiles. Bioinformatic analysis on example profiles showed the resolved proteoforms involved alternative splicing, proteolytic processing, glycosylation and ubiquitination, fragmentation, and probably transmembrane structures. Profile-based differential analysis revealed that many of the disease-caused changes were proteoform dependent. This work provided a proteome-scale view of protein distributions in SDS-PAGE gels, and the method would be useful to obtain proteoform-correlated information for in-depth proteomics.
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Affiliation(s)
- Changming Chen
- Institute of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, Guangzhou 510006, P. R. China
| | - Meiling Wen
- Institute of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, Guangzhou 510006, P. R. China
| | - Ya Jin
- Institute of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, Guangzhou 510006, P. R. China
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